LLMOps & Agent Frameworks: A Practical Guide to Building Production-Ready AI Features

Why LLMs need their own ops (LLMOps)

We’ve all shipped classical ML before — train, validate, deploy, monitor. LLMs flip this playbook. Instead of static models with well-bounded inputs, we deal with:

• Non-deterministic outputs
• Context windows instead of fixed features
• Cost per request tied to tokens
• Security/privacy issues from unstructured prompts

That’s why LLMOps exists: a lifecycle discipline for deploying, monitoring, and improving large language models in production. Like MLOps, but tuned for prompts, retrieval, and human-in-the-loop debugging.

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What agent frameworks do and when to use them

LLMs are powerful, but limited — they hallucinate, forget context, and lack structured reasoning. Agent frameworkswrap an LLM in tools, memory, and orchestration logic. The LLM decides what to do next; the framework enforces structure.

Popular frameworks:

• LangChain — batteries-included, wide ecosystem
• Haystack — research-leaning, strong retrievers
• Semantic Kernel — Microsoft-backed, integrates with enterprise stack

When NOT to use an agent

• Simple, deterministic flows (e.g., FAQ bot, form filler)
• Ultra-low-latency tasks (agents often chain multiple calls)
• Regulatory-critical apps (harder to audit agent reasoning)

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Core components of a production LLM architecture

Here’s a minimal view of production-ready LLMOps:

          ┌─────────────┐
          │  Client App │
          └──────┬──────┘
                 │
        ┌────────▼────────┐
        │  API Gateway    │
        └──────┬──────────┘
               │
 ┌─────────────▼─────────────┐
 │  Orchestration Layer      │
 │  (Agents, LangChain etc.) │
 └───────┬─────────┬─────────┘
         │         │
 ┌───────▼───┐ ┌───▼────────┐
 │ Vector DB │ │ LLM Hosting│
 │ (e.g.,    │ │ API or     │
 │ Pinecone) │ │ private)   │
 └───────────┘ └────────────┘

Model selection & hosting (API vs private models)

• API-hosted (OpenAI, Anthropic): fast start, but external data risk + cost exposure.
• Private hosting (vLLM, Ollama, HF models): control + compliance, but ops-heavy.

Vector DBs, retrieval pipelines, and prompt engineering patterns

• Vector DB (Pinecone, Weaviate, PGVector) for grounding.
• Retrieval-Augmented Generation (RAG) keeps answers fact-based.
• Prompt templates for consistency.

Orchestration & multi-agent patterns

• Single-agent loops for Q&A, summarization.
• Multi-agent systems when tasks need division of labor (planner, retriever, executor).

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Observability, testing, and CI for LLMs

Unlike unit tests, here we track behavior:

Metrics to collect:

• Token usage per request
• Latency distribution
• Failure / empty response rate
• Grounded vs hallucinated answers

Example CI test:

# Run automated eval on sample prompts
npm test:llm

// simple test in Jest
test("catalog query", async () => {
  const answer = await agent.ask("Do you sell red shoes?");
  expect(answer.toLowerCase()).toContain("yes");
});

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Security, data privacy, and compliance checklist

• Strip or mask PII before prompts.
• Rate-limit requests to prevent abuse.
• Run red-team prompts (“ignore instructions…”) to test jailbreaks.
• Log inputs/outputs securely (encrypt at rest).
• Compliance: GDPR, HIPAA if handling sensitive data.

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Cost control & scaling strategies

• Caching: store embeddings and LLM responses.
• Context window design: trim irrelevant docs, avoid “stuff everything” prompts.
• Batching: group embedding requests.
• Model tiering: cheap model for routing, expensive one for final answer.

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Hands-on example — build a simple agent that answers product-catalog questions

Let’s wire up a minimal catalog QA agent in Node.js:

npm init -y
npm install openai pg vector-search

index.ts

import OpenAI from "openai";
const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

// Fake vector search
async function vectorSearch(query: string) {
  const catalog = [
    { id: 1, text: "Red running shoes size 42" },
    { id: 2, text: "Blue hiking backpack 30L" },
  ];
  return catalog.filter((c) => c.text.toLowerCase().includes(query.toLowerCase()));
}

async function agentLoop(question: string) {
  const docs = await vectorSearch(question);
  const context = docs.map(d => d.text).join("\n");

  const prompt = `You are a product assistant.
Use only this catalog:\n${context}\n
Question: ${question}\nAnswer:`;

  const res = await client.chat.completions.create({
    model: "gpt-4o-mini",
    messages: [{ role: "user", content: prompt }],
  });

  return res.choices[0].message?.content;
}

// Example run
agentLoop("Do you have red shoes?").then(console.log);

Expected output:

Yes, the catalog includes red running shoes in size 42.

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Real-world tradeoffs and post-mortem checklist

Post-launch, ask:

• Are we blowing through token budgets?
• Do answers drift over time?
• Is latency acceptable at p95?
• Are logs auditable for compliance review?

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Further reading and tools

• LangChain docs
• Stack Overflow 2025 developer survey
• vLLM project

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FAQs

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Conclusion

LLMOps is about discipline, not hype. By combining observability, cost control, and careful architecture, we can ship LLM-powered features safely.